3 papers
math.OC2026
A Unifying View of Distributionally Robust Optimization for Probability Smoothing via Maximum Entropy
Kota Kurihara, Yuya Yamakawa, Yoichi Izunaga
The probability smoothing problem seeks estimators that assign positive probability to all categories, including those unobserved in a finite sample. In our previous work, we estab…
math.OC2025
An Axiomatic Analysis of Distributionally Robust Optimization with -Norm Ambiguity Sets for Probability Smoothing
Yoichi Izunaga, Kota Kurihara, Hokuto Nagano +1
We analyze the axiomatic properties of a class of probability estimators derived from Distributionally Robust Optimization (DRO) with -norm ambiguity sets (-DRO), a principle…
math.OC2025
A scalable mixed-integer conic optimization approach to cardinality-constrained Poisson regression with safe screening
Kota Kurihara, Yoichi Izunaga
This paper introduces a novel approach for cardinality-constrained Poisson regression to address feature selection challenges in high-dimensional count data. We formulate the probl…